A Chevy Convertible & Attribution: Both Offer Top-Down Technology

For some of us, summer is a time to take a relaxing ride down a long, scenic roadway and leave our worries behind for a few hours. And for those who are lucky enough to drive a convertible, it's very clear how much "top-down" technology enhances this experience.

Marketers have their own "top-down" technology -- called top-down attribution -- that can be used to enhance the performance of their marketing portfolios.

Why Put the Top Down?

Understanding top-down attribution starts with understanding its name. "Top-down" refers to utilizing a 50,000-foot level view of the marketing data to work toward specific insights and recommendations that enable marketers to perform cross channel marketing portfolio optimization. Bottom up attribution, on the other hand, utilizes more detailed 50-foot level data to work toward its own insights and recommendations. Specifically, top-down attribution is utilized when any of the channels with which you are working:

· Do not contain data with unique individual IDs to enable the linking of it to other touchpoints in the marketing touchpoint stack

· Have missing or inconsistent (can't be trusted) cookie data

What Fuel Does It Use?

Top-down attribution can utilize "summary" level data that simply provides counts of individuals who were exposed to and/or took action upon various marketing initiatives, broken down to specific levels of detail - but without any unique identifying IDs or cookies. For example, a slice of the total summary level data might state that 500K people were exposed to a given TV ad, 100K of whom viewed it on a given TV station, 50K of whom were located in a given DMA, 1K of whom viewed it on a particular date.

· Data pertaining to marketers' business policy changes over time, such as: changes in pricing, discounts, new products, special events, tightening or loosening of customer credit criteria, etc.

· Marketers' business rules and data taxonomy

Once the datasets above are collected from all the internal and external sources where they are available, the data is cleaned and normalized to a common set of key performance metrics so that it can be compared and analyzed in an apples-to-apples manner across all channels based on your individual business goals. After that process is complete, statistical algorithms such as regression and neural networks are applied to the data to calculate the relationships between every variable and dimension.

The byproduct of that mathematical process is a statistical model that attributes monetary credit to every variable and dimension of all your marketing initiatives to which your target audience has been exposed, and applies that credit to each of your defined business goals. But more than that, the model also enables you to plug numbers into a graphical user interface in a "what-if?" scenario manner to predict the outcome of potential future investments across your marketing portfolio. That's putting the keys to a powerful vehicle in the hands of the marketer.

Why Is It Better Than Your Father's Convertible?

Top-down attribution differs from these more commonly known forms of cross channel analysis such as:

· Marketing Mix Modeling -- which typically provides a one-time, snapshot-in-time analysis using marketing campaign information, econometrics and marketers business policy changes. Its output is usually in the form of PowerPoint slides or some other static document that details its findings, rather than an interactive model that provides ongoing insights.

· Media Mix Modeling -- which provides a one-time, snapshot-in-time analysis whose output is in the form of a document. But it focuses on media buys rather than the overall marketing portfolio. It analyzes media buys such as TV, Radio, OOH, search and display advertising, but typically not channels such as PR, social, events, etc.

In fact, top-down attribution is an iterative process that creates a virtuous cycle into which the latest data is continually fed and the statistical model is continually tested and refined so marketers always have up to date analysis and insights to drive their "what-if?" scenario planning, as well as steer their portfolio optimization process.

So roll back the ragtop, put on your shades, and consider taking top-down attribution out for a spin.

Best practice marketing optimization today is about modeling consumer behavior (people) vs. data AND involves software for ongoing use vs. one-shot PowerPoint reports done once-a-year. Bottom up attribution (aka modeling consumer behavior) is the only way to understand how different consumers (people) respond to the collective influence of marketing. Top down attribution is not a leap forward because it is still about modeling data (vs. people) and is still your father’s convertible.

Hi Jeff: While bottom-up attribution (or modeling the behavior of each individual consumer) is the best and most accurate model, it poses some practical limitations for certain marketing channels such as TV, Radio, Print and OOH. Current publisher technologies do not allow marketers to tie the impressions of offline channels to online channels with a unique ID on a user-by-user basis. The information available from offline channels is limited to aggregate data. We use advanced modeling techniques to find accurate halos, correlations and affinities. They are dynamically updated and the results are available over the web on 24x7 basis.

At Visual IQ, we recommend our clients to use bottom-up attribution for intra-channel insights and also to collectively model across digital channels, direct mail and telemarketing -- where individual consumer level data is available. Top-down modeling is prescribed for cross channel attribution that includes all channels – online and offline –, where some channels may not have consumer-level data.

I have interest in a tit-for-tat via web posting, but feel compelled to challenge what you are saying because it's just not correct. It's actually the opposite of what you claim...bottom up modeling has less limitations and excels at modeling all marketing influences on consumer behavior, including tv, print, radio....any & all online, offline, sponsorship, in-store promotion, etc.

It can do this because it translates all TRPs, clicks, redemptions, reach, etc into probability of exposure and is informed by how those probabilities translate into influence on purchase probability.

The data is fairly easy to source and customers don't often have to have it.